How to Create a Histogram from Pandas DataFrame?

A histogram is a graph that displays the frequency of values in a metric variable's intervals. These intervals are referred to as "bins," and they are all the same width.

We can create a histogram from the panda's data frame using the df.hist() function.

Syntax:

DataFrame.hist(column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, backend=None, legend=False, **kwargs)

Example 1: Creating a basic histogram( histogram for individual columns)

We use df.hist() and plot.show() to display the Histogram.

CSV file used: gene_expression.csv

# import libraries and packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# reading the CSV file
df = pd.read_csv('gene_expression.csv')

# displaying the DataFrame
print(df)

# creating a basic histogram
df.hist()
plt.show()

Output:

Example 2: Creating a modified histogram(plotting histogram by the group)

In this example, we add extra parameters to the hist method. We have changed the fig size, no of bins is specified as 15, and by parameter is given which ensures histograms for each cancer group are created.

# import libraries and packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# reading the CSV file
df = pd.read_csv('gene_expression.csv')

# displaying the DataFrame
print(df)

# creating a basic histogram
df.hist(by='Cancer Present', figsize=[12, 8], bins=15)
plt.show()

Output: